Google rewarded me today with this article written by Joe Armstrong, What’s all this fuss about Erlang?. It’s chucked filled with great information and additional resources. I also took a peek at his PhD thesis, worth taking a look at if you have the time.

Today I got kinda interested in learning more about machine learning. I’ve work with simple neural networks, done a tiny bit of Bayesian networks and nothing with genetic programming so either way it should be pretty fun.

I put together a page on this topic at Sugar Code that has a bunch of books that look promising, plus some of them can be bought for next to nothing.

Also Peteris Krumins has an excellent page with a bunch of online video lectures that I’m going to start working through.

I’ve added two java lessons, one math lesson, and two computer lessons (6 more are done, just being lazy on putting them up) on my new wiki, SugarCode. The wiki is still in it’s early skeleton phases, plenty of ToDo’s can still be found but fell free to check it out.

Found this posted on /r/programming this morning. The course is taught by Yaser Abu-Mostafa and looks pretty sweet. The closet thing I’ve ever done in ML was playing around with Neural networks comparing Jets and Sharks but I’ll throw in a few extra links just encase anyone finds them useful.

Learning From Data
This site contains the central topics covered in Machine Learning Course – CS 156. The video lectures are spliced around the focal points and seemed to be around 30 minutes.http://work.caltech.edu/library/

Machine Learning Course – CS 156 Youtube Channel
This is the link for Caltech’s channel of video lectures. The lectures seemed to be around a hour and half which is awesome. They start out with a review and end in a Q&A.CalTech CS 156

Here are a few of my neural nets links.
There is a pycon presentation that covers this code sample written by Raymond Hettinger. I’m not entirely sure what year, I think 2011, maybe 2010 if that helps in any internet searching.Data Mining with Neural Nets

An Introduction To Neural Networks
This is an IBM developerWorks article written by Andrew Blais and David Mertz so it’s a must read.